Experimentation and optimization of wear behavior on polytetrafluoroethylene composites using response surface methodology and bald eagle search optimization

Abstract

In Industries, polytetrafluoroethylene (PTFE) is used as a solid lubricant due to its excellent toughness, lack of reactivity, high-thermal stability, and so forth. However, the abrasive resistive behavior is minimal for the virgin PTFE polymer. In this study, the wear and coefficient of friction (COF) of PTFE composite prepared by the combination of 10% halloysite, 10% graphite, and 10% bronze are investigated. The investigations are performed on a pin-on-disc test rig and analysis is done by response surface methodology (RSM), especially in Box-Behnken Design (BBD). The experimentation is performed by varying the sliding distance (Ds), sliding velocity (Vs), and applied load (L). In the analysis of COF, the load is considered as a significant parameter, and sliding distance is an influencing parameter for wear rate. From the experimentation, the proposed PTFE composite promotes COF of 0.0052 and a wear rate of 0.62 Â 10 À5 mm 3 /Nm. Such observed wear rate and COF are 2.8 times and 1.5 times better than the pure PTFE. The experimented outcomes are validated using hybrid DBN-based bald eagle search optimization (DBN-BESO), and RSM. From the validated results, the proposed hybrid DBN-BESO prediction process using the Matlab platform promotes closer values to the experimented outcomes. Besides, the parametric optimization is conducted using RSM and BESO approaches with the objective of minimum wear rate and COF. The optimized BESO results are superior to the RSM. BESO-based optimized wear rate is 0.0317 Â 10 À5 mm 3 /Nm and COF is 0.0352.

Structural design optimization of CFRP/Al hybrid co‐cured high‐speed flywheel with the particle swarm optimization algorithm

“…[25] Among them, the particle swarm optimization algorithm (PSOA) has a wide range of applications. [26][27][28][29] Especially in the field of structural optimization, the PSOA has attracted the attention of many scholars. The performance of the PSOA was argued by Fourie et al [30] with standard size and shape design problems, which was taken as a comparison with other optimization methods.…”